An Activity-based Travel Demand Model for London

This paper describes how there has been considerable interest focused on the development of activity-based travel demand modeling techniques as an alternative to conventional trip-based approaches in recent years. Among the benefits claimed for these approaches are greater behavioral realism and credibility in the prediction of travelers’ behavioral response, especially to non-marginal policy measures. Work in North America and in some European countries has led to the development and implementation of significant activity-based demand modeling systems, which are being used for operational policy analysis. However, in the United Kingdom (UK) the practical application of activity based modeling techniques has been much slower. This paper reports on the first large scale empirical application of an activity-based travel demand modeling system for Greater London. The initial application focuses on the development of a household-based activity scheduling model that predicts the activities that household members engage in, and schedules the activities both temporally and spatially. The modeling approach is based on TASHA, a rule-based activity scheduling model. In addition to customizing TASHA to the London study area, the paper proposes to extend the scheduling approach adopted in TASHA to align the treatment of choice behavior more systematically with random utility theory by relaxing several of the restrictive and deterministic rules in the activity scheduling process. The paper first presents the process of data assembly for the activity based travel demand model system, which combined time use and travel diary data. This is followed by a description of the model development and application. Initial results from the empirical application of the model are also presented, and validated against the results of the London Transportation Studies (LTS) model. Several policy scenarios are then examined, such as the congestion pricing policy and increased public transport costs, to explore the differences between the traditional trip-based LTS and the activity-based approach. This research is part of a broader line of study into the understanding of the ways people’s social and economic behavior in cities result in aggregate energy demands, as an interim step towards developing strategies for improving urban energy efficiency.